{"data":{"id":"4915f47d-9608-4b51-bf03-4c213203aa44","title":"From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs","summary":"This article examines how large language models (AI systems trained on huge amounts of text data) can be used in cybersecurity red teaming (simulated attacks to test defenses) and blue teaming (defensive security work), mapping their abilities to established security frameworks. However, LLMs struggle in difficult, real-world situations because they have limitations like hallucinations (generating false information confidently), poor memory of long conversations, and gaps in logical reasoning.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"http://ieeexplore.ieee.org/document/11435543","publishedAt":"2026-03-16T13:26:53.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-03-16T13:26:53.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity","safety"],"aiComponentTargeted":"model","llmSpecific":true,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}